This is the story of how an iconic company founded more than a century ago, and once deemed a “dinosaur” that would not be able to survive the 1990s, has learned lesson after lesson about survival and transformation. The use of analytics to bring more science into the business decision process is a key underpinning of this survival and transformation. Now for the first time, the inside story of how analytics is being used across the IBM enterprise is being told. According to Ginni Rometty, Chairman, President, and Chief Executive Officer, IBM Corporation, “Analytics is forming the silver thread through the future of everything we do.” What is analytics? In simple terms, analytics is any mathematical or scientific method that augments data with the intent of providing new insight. With the nearly 1 trillion connected objects and devices generating an estimated 2.5 billion gigabytes of new data each day,1 analytics can help discover insights in the data. That insight creates competitive advantage when used to inform actions and decisions. Data is becoming the world’s new natural resource, and learning how to use that resource is a game changer.

This book will help you chart your own course to using analytics as a smarter way of driving outcomes. To get the most value from analytics, start with the strategy you are executing and apply analytics to your most important business problems. If you have thought of analytics as only a technology, this book will change that. Analytics is not just a technology; it is a way of doing business. Through the use of analytics, insights from data can be created to augment the gut feelings and intuition that many decisions are based on today. Analytics does not replace human judgment or diminish the creative, innovative spirit but rather informs it with new insights to be weighed in the decision process. Michael Lewis, in his book Moneyball: The Art of Winning an Unfair Game, describes how even in baseball, which is rooted in statistics, analytics enabled the Oakland Athletics to assemble a competitive baseball team, despite paying the third-lowest salaries.2 Analytics for the sake of analytics will not get you far. To drive the most value, analytics should be applied to solving your most important business challenges and deployed widely. Analytics is a means, not an end. It is a way of thinking that leads to fact-based decision making.

“We believe that analytics is no longer an emerging field; today’s businesses will thrive only if they master the application of analytics to all forms of data. Whether your office is a scientific lab, a manufacturing company, an emergency room, a government agency, or a professional sports stadium, there is no industry left where an analytics-trained professional cannot make a positive impact,” says Brenda Dietrich, IBM Fellow and Vice President, Emerging Technologies, IBM Watson.

The intent of this book is to take some of the mystery out of how an organization can leverage big data and analytics to achieve its goals by giving current and future leaders a front-row seat to see how analytics was leveraged to transform IBM. Many consultants, academicians, and others have written eloquently on the topic of analytics, but the stories from within IBM as told by the people who learned lesson after lesson will give a real-world perspective on what works, what doesn’t work, and how you can either start or accelerate your own transformation journey.

Why IBM Started an Enterprise-Wide Journey to Use Analytics

IBM has been using what we now call analytics in manufacturing and product design since the late 1950s and in supply chain operations since the 1980s. A pivotal meeting took place in 2004 between Brenda Dietrich and Linda Sanford, then Vice President of Enterprise Transformation, IBM Corporation, when IBM expanded its use of analytics from physical applications, such as supply chain and manufacturing, to applications, such as sales and finance, that did not have processes with such obvious physical characteristics, and IBM’s enterprise-wide transformation journey to use analytics was launched.

In 2004, Dietrich led the Mathematical Sciences Department in IBM Research, a group that included a range of computational mathematics disciplines, including statistics, data mining, and operations research. Coincidentally, both Dietrich and Sanford received degrees in operations research, which is the practical application of math to real-world problems and was a precursor to much of what is now called business analytics. Sanford had seen the value of the mathematical methods developed in the Mathematical Sciences Department applied to IBM’s supply chain operations.

Sanford’s transformation team was looking for opportunities to build more analytics capability into IBM’s overall transformation. She knew they had to put measurable successes on the board early to create a sense of credibility for their work. Dietrich and Sanford discussed the IBM sales process and the simple, easily tracked metric annual revenue per IBM seller. The goal was to increase the numerator, to generate top-line growth for the company. They started with a small pilot program, working with sales representatives in the general business group in Canada. That initial pilot was able to use IBM internal data, along with publicly available data, to score sales opportunities. The immediate results were a higher-yield pipeline for the sellers and improved revenue per seller. More importantly, they proved the power of analytics to support growth and transformation.

IBM has been an avid consumer of analytic capabilities for the past decade. Use of analytics has spread from engineering-based processes, such as product design, through logistics processes, such as supply chain operations, to human-centric processes, such as sales and workforce management. Seeing the cultural shift in the receptiveness to the use of analytics has been amazing to see. When IBM started developing sales analytics tools, many sales leaders were skeptical about the value of such tools, believing that converting an opportunity into a sale was largely a function of the seller’s actions and could not be predicted in advance. But over the past decade, there has been a sea change in attitude. Now sales managers are asking for more analytics support so they can take their organizations to the next level of performance.